



'''simple docstring'''

import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 				a  (     UpperCamelCase_    :    str			)       ->	Optional[Any]:
		if "model" in orig_key:
				snake_case__	     =orig_key.replace('model.'      ,					''			)
		if "norm1" in orig_key:
				snake_case__	     =orig_key.replace('norm1'      ,					'attention.output.LayerNorm'			)
		if "norm2" in orig_key:
				snake_case__	     =orig_key.replace('norm2'      ,					'output.LayerNorm'			)
		if "norm" in orig_key:
				snake_case__	     =orig_key.replace('norm'      ,					'LayerNorm'			)
		if "transformer" in orig_key:
				snake_case__	     =orig_key.split('.'			)[0].split('_'			)[-1]
				snake_case__	     =orig_key.replace(f"""transformer_{layer_num}"""      ,					f"""encoder.layer.{layer_num}"""			)
		if "mha.attn" in orig_key:
				snake_case__	     =orig_key.replace('mha.attn'      ,					'attention.self'			)
		if "mha" in orig_key:
				snake_case__	     =orig_key.replace('mha'      ,					'attention'			)
		if "W_q" in orig_key:
				snake_case__	     =orig_key.replace('W_q'      ,					'self.query'			)
		if "W_k" in orig_key:
				snake_case__	     =orig_key.replace('W_k'      ,					'self.key'			)
		if "W_v" in orig_key:
				snake_case__	     =orig_key.replace('W_v'      ,					'self.value'			)
		if "ff1" in orig_key:
				snake_case__	     =orig_key.replace('ff1'      ,					'intermediate.dense'			)
		if "ff2" in orig_key:
				snake_case__	     =orig_key.replace('ff2'      ,					'output.dense'			)
		if "ff" in orig_key:
				snake_case__	     =orig_key.replace('ff'      ,					'output.dense'			)
		if "mlm_class" in orig_key:
				snake_case__	     =orig_key.replace('mlm.mlm_class'      ,					'cls.predictions.decoder'			)
		if "mlm" in orig_key:
				snake_case__	     =orig_key.replace('mlm'      ,					'cls.predictions.transform'			)
		if "cls" not in orig_key:
				snake_case__	     ='yoso.' + orig_key

		return orig_key






def 				a  (     UpperCamelCase_    :    Tuple      ,					UpperCamelCase_    :    str			)       ->	Dict:
		for key in orig_state_dict.copy().keys():
				snake_case__	     =orig_state_dict.pop(UpperCamelCase_			)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						snake_case__	     =val

		snake_case__	     =orig_state_dict['cls.predictions.decoder.bias']
		snake_case__	     =torch.arange(UpperCamelCase_			).expand((1, -1)			) + 2

		return orig_state_dict






def 				a  (     UpperCamelCase_    :    Dict      ,					UpperCamelCase_    :    int      ,					UpperCamelCase_    :    Any			)       ->	List[str]:
		snake_case__	     =torch.load(UpperCamelCase_      ,					map_location='cpu'			)['model_state_dict']
		snake_case__	     =YosoConfig.from_json_file(UpperCamelCase_			)
		snake_case__	     =YosoForMaskedLM(UpperCamelCase_			)

		snake_case__	     =convert_checkpoint_helper(config.max_position_embeddings      ,					UpperCamelCase_			)

		print(model.load_state_dict(UpperCamelCase_			)			)
		model.eval()
		model.save_pretrained(UpperCamelCase_			)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""			)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__     :						Tuple	=  argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The json file for YOSO model config.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			SCREAMING_SNAKE_CASE__     :						Any	=  parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




